Wavelet-based artificial neural net combustion sensing

ABSTRACT

A method and apparatus for real-time measurement of combustion characteristics of each combustion event in each individual cylinder coupled with an ability to control the engine based upon the combustion characteristics are shown. The invention includes using selective sampling techniques and wavelet transforms to extract a critical signal feature from an ionization signal that is generated by an in-cylinder ion sensor, and then feeds that critical signal feature into an artificial neural network to determine a desired combustion characteristic of the combustion event. The desired combustion characteristic of the combustion event includes a location of peak pressure, an air/fuel ratio, or a percentage of mass-fraction burned, among others. The control system of the engine is then operable to control the engine based upon the combustion characteristic.

INCORPORATION BY REFERENCE

Applicant incorporates by reference U.S. Pat. No. 6,367,462, entitledEngine Torque Management Method with High Dilution EGR Control, issuedto McKay, et al., in that the method for engine torque management neednot be fully described in detail herein.

TECHNICAL FIELD

This invention pertains generally to internal combustion engine controlsystems, and more specifically to real-time digital signal processingfor engine control and diagnostics.

BACKGROUND OF THE INVENTION

There is a need to be able to effectively collect and analyze datarelated to combustion characteristics of an internal combustion engineand to control the engine based upon that data. Current engine controlsystems use exhaust gas sensors, primarily oxygen sensors, to providefeedback about the overall combustion operation of the engine. Otherfeedback devices that have been proposed for engine control systemsinclude in-cylinder pressure sensors and in-cylinder temperature sensors

A combustion quality measurement technique utilizing flame ionizationdetection wherein a spark plug is also used as a sensor has been inproduction for some time. The ionization signal is a measure of changesin electrical conductivity of a combustion flame front that is createdin a cylinder during each combustion cycle. As shown in FIG. 4,information that is gleaned from an ionization signal includes locationof a peak combustion pressure, air to fuel ratio, or percentage of massfraction burned. Present versions of the sensor and measurement systemare currently used for control and diagnostic purposes. These systemsinclude detection and control of charge pre-ignition or ‘knock’,detection of engine misfire, and control of cam phasing systems.

The ability to use an ionization signal for engine control is limited bythe ability to glean critical signal features from the signal.Fluctuations in the ionization signal caused by variations within anengine, engine to engine variations, and external factors have made morecomplete interpretation and utilization of the ionization signaldifficult. Variations within an engine that affect the combustionprocess include engine operating temperature, cylinder-to-cylindermaldistribution of air, fuel, and EGR, spark timing and energy, and theage and level of deterioration of the components, among others.Variations between engines that affect the combustion process, and hencethe ionization current, include part-to-part differences, vehicleapplication differences, and operator usage differences, and componentage. Variations in external factors that affect the combustion processinclude in-use fuel type, use of fuel additives, ambient air humidity,ambient temperature, and elevation. These factors, among others, make itdifficult to perform a straightforward interpretation of an ionizationsignal created as an output of the combustion process.

The prior art has been unable to accomplish demonstrable advanced enginecontrol and engine diagnostic capability using information from anionization signal. The prior art has been unable to provide real-timesignal processing that leads to information related to critical signalfeatures such as the location of peak pressure, air to fuel ratio, orpercentage of mass fraction burned, when measured over a wide range ofengine operating conditions. The prior art has not been robust tochanges in conditions that affect measured engine operating conditions,including external conditions such as fuel quality and ambienttemperature. The prior art also has not been robust to changes inoperating conditions such as engine operating temperature and variationsin in-cylinder temperatures.

There is a need to be able to more completely determine combustioncharacteristics from an ionization signal to make it useful as a systemfor combustion control. Conventional analytical methods have notprovided a level of robustness necessary for mass production applicationof an ionization system. The prior art has attempted to solve theproblem using artificial neural networks (ANN) for analyticalinterpretation of ionization signals. A properly trained ANN-basedionization sensor and system has been shown to be able to accommodatecombustion fluctuations. A comprehensive training of the ANN thatcovered a broad range of possible engine operating conditions hasenhanced performance of an engine control system. A limitation ofartificial intelligence is that an ANN device only knows what it wastaught; it can not extrapolate beyond the range of its training, nor canit perform any better than it was taught during training. Training ofthe ANN also consumes time both to collect appropriate data sets fortraining, and to train so that it can acquire effective coefficients andbiases for internal equations. The ANN also takes an amount of time toprocess the input array and provide an output. An ANN works effectivelyonly if the pre-production algorithm formulation and ANN trainingresulted from an experimental data set representing all future engineoperating conditions. In practice, this might be impossible or at leastextremely time and resource consuming. A reasonable solution can insteadinclude a limited training with well chosen, most-representative sets ofoperating conditions. This can be combined with a fuzzy logic block thatoverrules unusual sensor readings to control an engine.

The prior art has implemented ANN using a dedicated digital signalprocessing (DSP) electronic chip implemented in the controller, as wellas using algorithms. Dedicated ANN DSP chips can be costly, and aregenerally dedicated to a specific application, which limits theflexibility of the device, and makes the operating characteristics ofthe ANN difficult to change.

The prior art has also sought to use statistical analysis tools such asprincipal component analysis (PCA). The PCA method generates a new setof input vector components from a linear combination of original vectorcomponents. All the new components are orthogonal to each other so thereis no redundant information. However, it is commonplace for the sum ofthe variances of the first few new components to almost match the totalvariance of the components of the original vector. The PCA methodentails the need to collect and process massive amounts of data toextract useful information from the input signal. The PCA methodrequires acquisition of a large quantity of data (vector array of 123input elements in one case), and takes an extended amount of time toreduce to a useful signal. This limits the throughput of the controller,and therefore the dynamic range over which the method is used to controla system.

Accordingly, a need exists for a more complete method to analyze theinput from an ionization signal, to extract critical signal featuresfrom the ionization signal, to determine combustion characteristics fromthe critical signal features, and to control an internal combustionengine over a wide range of operating conditions, using the combustioncharacteristics. There is a further need to have data acquisitionhardware and a controller that are flexible and meet the requirementsfor an automotive microprocessor system. Implementation of an enginecontrol system that determines a combustion characteristic based upon anionization signal can offer improvements in engine control anddiagnostics, including an ability to extract critical signal featuresincluding a location of peak cylinder pressure, air/fuel ratio, and EGRdilution fraction, among others. A system that analyzes input from anionization signal obtained through an in-cylinder plug can be used toreduce engine development and calibration time as well as provideopportunities to remove or redesign components such as knock sensors,exhaust gas sensors, cam sensors, and others.

SUMMARY OF THE INVENTION

The present invention provides an improvement over conventional enginecontrols in that it provides a method and apparatus for real-timemeasurement of combustion characteristics of each combustion event ineach individual cylinder coupled with an ability to control the enginebased upon the combustion characteristics. The invention includes usingselective sampling techniques and wavelet transforms to extract acritical signal feature from an ionization signal that is generated byan in-cylinder ion sensor, and then feeds that critical signal featureinto an artificial neural network to determine a desired combustioncharacteristic of the combustion event. The desired combustioncharacteristic of the combustion event includes a location of peakpressure, an air/fuel ratio, or a percentage of mass-fraction burned,among others. The control system of the engine is then operable tocontrol the engine based upon the combustion characteristic. Thisincludes control of engine torque, and more specifically fuel injection,exhaust gas recirculation, cam timing and phasing, as well as otherengine control elements. This also includes spark timing and dwell whenthe engine is a spark-ignition engine.

These and other aspects of the invention will become apparent to thoseskilled in the art upon reading and understanding the following detaileddescription of the embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention may take physical form in certain parts and arrangement ofparts, the preferred embodiment of which will be described in detail andillustrated in the accompanying drawings which form a part hereof, andwherein:

FIG. 1 is a descriptive view of an engine and control system, inaccordance with the present invention;

FIG. 2 is a functional block diagram, in accordance with the presentinvention;

FIG. 3 is another functional block diagram, in accordance with thepresent invention; and

FIG. 4 is a graphical representation of data, in accordance with thepresent invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Referring now to the drawings, wherein the showings are for the purposeof illustrating the preferred embodiment of the invention only and notfor the purpose of limiting the same, FIG. 1 shows an internalcombustion engine 5 and controller 10 which have been constructed inaccordance with an embodiment of the present invention. In thisembodiment, the internal combustion engine is a spark-ignition engine.The internal combustion engine 5 is comprised of at least one cylindercontaining a piston that is operably attached to a crankshaft at a pointthat is eccentric to an axis of rotation of the crankshaft. There is ahead at the top of the piston containing valves for intake and exhaustair and a spark plug. A combustion chamber is formed within the cylinderbetween the piston and the head. A combustion charge comprising acombination of air and fuel is inlet through the intake valve into thecombustion chamber, and is ignited by the spark plug, according topredetermined conditions. The ignition of the air and fuel causes anincrease in pressure in the combustion chamber, which forces the pistonto move linearly along the length of the cylinder, away from the head.The movement of the piston in turn causes the crankshaft to rotate. Thecrankshaft causes the piston to again move toward the head after thecrankshaft has rotated to a furthest point of eccentricity. Theoperation of a spark-ignition internal combustion engine is well knownto one skilled in the art.

The internal combustion engine 5 is configured with sensors that areoperable to measure engine performance, and output devices that areoperable to control engine performance. The sensors may include, forexample, an engine speed sensor, a manifold absolute pressure sensor, athrottle position sensor, a mass air flow sensor, an intake air sensor,an EGR position sensor, an exhaust pressure sensor, an oxygen sensor orother exhaust gas sensor, torque sensor, a combustion sensor, or a camposition sensor (not shown). The engine speed sensor is used todetermine engine rotational speed; the manifold absolute pressuresensor, the throttle position sensor, the intake air temperature sensor,and the mass air flow sensor is used to determine engine load; and theEGR position sensor is used in conjunction with the manifold absolutepressure sensor to determine a mass fraction of EGR that is delivered tothe engine. The output devices may include, for example, a fuelinjection system, a spark ignition system, an electronic throttlecontrol system, an exhaust gas recirculation system, an evaporativecontrol system, or a variable cam phasing system (not shown). Thecontroller 10 collects information from the sensors to determine engineperformance parameters and controls the output devices, using controlalgorithms and calibrations internal to the controller 10.

Alternatively, the controller 10 may be operably connected to at leastone sensor and an engine torque management system (as described in U.S.Pat. No. 6,367,462, entitled Engine Torque Management Method with HighDilution EGR Control, issued to McKay, et al., and is incorporated byreference herewith). The engine torque management system may comprise afuel injection system, an ignition system, an electronic throttlecontrol system, an exhaust gas recirculation system, an evaporativecontrol system, and a variable cam phasing system (not shown). Thesensors are those as described previously. The controller 10 controlsthe engine torque management system based upon input from the sensor.Mechanization and operation of an internal combustion engine usingsensors, output devices, and the controller 10 is well known to oneskilled the art.

The spark ignition system of this embodiment comprises the spark plug 14and ignition coil (not shown) that are electrically connected to an ionsense ignition module 12 that is preferably operably integrated into thecontroller 10. The spark plug 14 serves dual functions. It is operableto ignite a combustion charge in a cylinder (not shown) when a sparkcontrol signal is sent from the controller 10 through the ion senseignition module 12. It is also operable to sense an ionization signalresulting from the ignition of a combustion charge. A combustion eventis defined by the action of the intake of an air/fuel charge into thecylinder (not shown), compressing the charge, igniting the charge toextract power from the charge, and finally the exhaust of the chargefrom the cylinder. The ion-sense ignition system mechanization andoperation, and the combustion process are well known to one skilled inthe art.

The engine control system is preferably configured in such a manner thatthe controller 10 samples an ionization signal (see FIG. 4) from eachcombustion event of the engine 5 using the spark plug 14 and theion-sense ignition module 12. The controller 10 samples the ionizationsignal and concurrently determines specific engine operating parameters,using the engine sensors (not shown) and the spark plug 14 mentionedpreviously. The controller 10 then transforms the ionization signal witha wavelet filter 24. The transformed ionization signal that is outputfrom the wavelet filter 24 is passed through a feature extractor 28,which operates to extract one or more critical signal features from theionization signal. The controller 10 then processes the critical signalfeatures and the engine operating parameters through an artificialneural network 32 to determine a combustion characteristic of the engine5. The combustion characteristic is determined each combustion event.The controller 10 then controls the output devices of the internalcombustion engine 5 based upon the combustion characteristic that wasdetermined using the neural network 32.

Referring now to FIG. 2, a block diagram of an embodiment of method andsystem for wavelet-based artificial neural net combustion sensing isshown. This embodiment represents the functional implementation of thewavelet-based artificial neural net combustion sensing system that isconfigured in the controller 10 in the form of algorithms andcalibrations. There is an analog data sampler 20 that gathers data fromthe ionization signal that comes from each spark plug 14. There is asampling and processing scheduler 22 that causes the analog data sampler20 to concurrently take data from the ionization signal and variousengine sensors (not shown) to determine engine operating parameters.This data is used to operate a feature extraction mode, and also tooperate the artificial neural net 32. An output of the analog datasampler 20 is a digitized signal of the ionization signal and isprovided as input to a wavelet filter 24, which conducts a multileveldecomposition of the output of the analog data sampler 20 and anaccompanying reconstruction of the signal, as shown in blocks 34, 36.The output of the multilevel decomposition block 34 is compressed andstored for training of the artificial neural net 32, as shown in block26. An output of the reconstruction block 36 is input to the featureextractor 28, which acts to extract one or more critical signal featuresfrom the ionization signal that has been transformed by the waveletfilter 24. The engine operating parameters 30 and the critical signalfeatures of the ionization signal are input to the artificial neural net32. An output of the artificial neural net 32 is a combustioncharacteristic of the engine, which is used by the controller 10 tocontrol the engine 5.

The analog data sampler 20 captures a data sample from the ionizationsignal that comes from the spark plug 14. Sampling of the ionizationsignal is scheduled to focus on an initial phase of combustion processin order to extract information that is related to a specific combustioncharacteristic. This is shown in FIG. 4. The sampling and processingscheduler 22 causes the analog data sampler 20 to sample the ionizationsignal during the period of time immediately after a spark ignitionevent, e.g. for 50 elapsed degrees of angular rotation of the crankshaft(not shown) after each spark ignition event (see item 130 of FIG. 4).The preferred sampling rate is to sample the ionization signal at leastonce per 0.5 degrees of rotation of the crankshaft (not shown) for eachcombustion event.

The data sample that is taken from the ionization signal is input to ananalog/digital converter (not shown) in the analog data sampler 20. Thedata sample is digitized and becomes an input to the wavelet filter 24.The sampling and processing scheduler 22 also causes the engineoperating parameters to be captured concurrently with the data sample.The engine operating parameters preferably include engine rotationalspeed, engine load, engine operating temperature, spark ignitionadvance, and intended EGR mass-fraction, and is determined by thecontroller 10, as described previously. This data is used in conjunctionwith the feature extractor 28 and operation of the artificial neural net32.

The wavelet filter 24 is a mathematical transform that recognizescritical signal features in the data sample, based upon principles ofshaping and scaling. The feature extractor 28 extracts the criticalsignal features from the data sample that has been transformed by thewavelet filter 24. A wavelet is defined as a waveform of limitedduration that has an average value of zero. A section of a sine wave canbe characterized using a wavelet. An ionization signal from an ionsensor, i.e. a spark plug, can also be characterized using a wavelet.The extracted patterns may not be readily identified in the originaldata sample due to noise. Each wavelet filter 24 and feature extractor28 is specially tuned to identify and extract a specific critical signalfeature from the data sample. Extracting a critical signal featurecomprises quantifying the critical signal feature in units of measure. Aspecific critical signal feature includes a spike in the signal or afirst or a second peak value. Critical signal features related to anionization signal are shown in FIG. 4, and include, for example, asecond peak pressure 140, measured in units of Pascals or Bar, and anintegrated flame phase 120, measured in units of volt-seconds, orvolts-crank-angle degrees. Tuning of a wavelet filter and a featureextractor to identify and extract a specific critical signal featurefrom a data sample is known to one skilled in the art.

The complete wavelet filter 24 is comprised of a multileveldecomposition segment 34 and a corresponding multilevel reconstructionsegment 36. The multilevel decomposition segment 34 is comprised of aseries of digital filters that are executed as algorithms in thecontroller 10. A wavelet transform decomposes the data sample in thedecomposition segment 34, compresses the data sample, and thenreconstructs the data sample in the reconstruction segment 36 forrecognition and extraction of a signal in the wavelet, using the featureextractor 28.

The decomposition and reconstruction segments 34, 36 are comprised ofhigh pass and low pass digital filter legs. The decomposition segment 34may be comprised of both a high pass filter leg and a low pass filterleg. Alternatively, the decomposition segment 34 may be comprised ofonly the high pass filter leg or only the low pass filter leg, dependingupon the signal feature to be extracted. For example, using only a lowpass filter leg for decomposition 34 followed by reconstruction 36allows for selective extraction of a low-frequency signal that is notnecessarily periodic.

The high pass filter leg divides the input to provide a detail contentof the data sample. The high pass filter leg is a series of high passdigital filters wherein the initial input is a digitized representationof the data sample from the ion sensor and each output of a filterbecomes the input to a subsequent filter. Each high pass filtergenerates a differential of the adjacent input signals using theequation:DIFF=constantA*[s(N+1)−s(N)]/2wherein constantA represents a predetermined coefficient based upontuning of the wavelet filter, N represents a count in the digital signalsequence, i.e. time or angular position, and s(N) represents the signalfrom the data sample at N. In this embodiment, a preferred value for Nis 0.5 degrees of angular rotation of the crankshaft (not shown). S(N)is a measure of current in microamperes at the specific position N, asdetermined by the ion-sense ignition module 12. The predeterminedcoefficient constantA may vary for each subsequent digital filter. Thepredetermined coefficient constantA is defined by the specific type ofwavelet filter that has been selected and the tuning associatedtherewith.

The low pass filter leg divides the input to provide an approximationcontent of the wavelet. The low pass filter leg is a series of low passfilters wherein the input is the aforementioned wavelet, and each outputof a filter becomes the input to a subsequent filter. Each low passfilter generates an average of the adjacent input signals using theequation:AVG=constantD*[s(N+1)+s(N)]/2wherein constantD represents a predetermined coefficient based upontuning of the wavelet filter, N represents count in the digital signalsequence, i.e. time or angular position, and s(N) represents the signalfrom the data sample at N. Again, in this embodiment a preferred valuefor N is 0.5 degrees of angular rotation of the crankshaft (not shown).S(N) is a measure of current in microamperes at the specific position N,as determined by the ion-sense ignition module 12. The predeterminedcoefficient constantD may vary for each subsequent digital filter. Thepredetermined coefficient constantD is also defined by the specific typeof wavelet transform filter that has been selected and the tuningassociated therewith.

Reconstruction (block 36) of the ionization signal occurs byreassembling each digital filter in a reverse order of the decompositionsegment. The output of the reconstruction is a full recovery of thecritical signal feature with unwanted noise eliminated, which is thenpassed through the feature extractor 28. The extracted signal is theninput to the artificial neural network 32 for further analysis andsubsequent use by the controller 10.

Portions of both the approximation content and the detail content areretained or discarded, which serves to enhance the critical signalfeature for later extraction during feature extraction, shown in block28. In block 28, the feature extractor acts to extract critical signalfeatures in the form of the detail content obtained from the data sampleusing the high pass filter leg of the wavelet filter 24. This includesextracting a discontinuity such as a spike, and comprises the shape andtime of occurrence of the spike. The feature extractor 28 also extractscritical signal features in the form of the approximation contentobtained from the data sample using the low pass filter leg of thewavelet filter 24. This includes extracting a curve shape descriptionthat may include the amplitude, frequency, and points of inflection. Theuse of wavelet filters and feature extractors to extract a criticalsignal feature from a data sample is known to one skilled in the art.

The critical signal features include, for example, a location of peakpressure or an integral of the flame phase. The location of peakpressure is a measure of the rotational position of the crankshaft (notshown) or a measure of elapsed time after the start of a spark eventwherein the ionization signal reaches a second peak value, which is alsoa first peak value in the ionization signal that occurs after the flamephase of the ionization signal (see item 140 of FIG. 4). The location ofpeak pressure is directly correlated to the location of the peakcylinder pressure, which is used by the controller 10 to determine amagnitude of engine torque and control the engine 5. An integral of theflame phase of the ionization signal is a time-integral of theionization signal during the flame phase of the combustion event, whichis the first peak of the combustion process after the start of sparkevent. This is shown as item 120 of FIG. 4. The integral of the flamephase is correlated to the instantaneous air/fuel ratio, which is usedby the controller 10 to control the engine 5.

As has been described, the output of the reconstruction 36 is input to afeature extractor 28, which acts to extract one or more critical signalfeatures from the ionization signal that has been processed by thewavelet filter 24. The engine operating parameters 30 collectedconcurrent with the ionization signal, and the critical signal featuresthat are output from the feature extractor 28 become inputs to the ANN32.

The ANN 32 is a sophisticated nonlinear analytical method used to modelcomplex relationships between an array of data inputs and at least oneoutput. In this embodiment, the array of data inputs is comprised of thecritical signal feature and specific engine parameters. The output isthe desired combustion characteristic. The ANN 32 is implemented as aseries of digital signal processing algorithms in the controller 10. Arelationship exists between the known array of data inputs and unknownoutput, but the exact nature of the relationships between inputs andoutput is unknown. The array of data inputs generally contain addednoise.

Referring now to FIG. 3, a setup for training the ANN 60 is shown, whichconsists of bench training on pre-production engines and vehicles. TheANN 60 requires training using an adaptive ANN 80 during development,and implementation of the trained ANN in the controller 10. Training ofthe adaptive ANN 80 occurs to establish a relationship between the arrayof data inputs and a desired output 84, using at least one supervisedlearning algorithm in the preferred embodiment. To train the adaptiveANN 80 a set of training data is assembled which contains examples ofthe array of data inputs together with the corresponding desired output84. The learning algorithm uses the training data to adjust coefficientsand biases used by the equations of the adaptive ANN 80 to minimize theerror in its predictions on the training data. When the adaptive ANN 80is properly trained it creates a series of inferred relationships thatrelate the array of data inputs to the output. The ANN 60 is implementedusing the coefficients and biases developed by the adaptive ANN 80.Successful implementation of the ANN 60 requires knowledge of how toselect and prepare input data and output data, how to select anappropriate neural network, and how to assess and interpret results.When implemented, the ANN 60 has fixed coefficients and biases for theequations based upon values developed by the learning algorithm duringtraining. Use of an ANN is known to one skilled in the art.

Referring again to FIG. 3, a setup for training the adaptive ANN 80 isshown, which consists of bench training on pre-production engines andvehicles (not shown). The training setup includes the adaptive ANN 80with a learning algorithm and internal equations, each with theaforementioned adjustable coefficients and biases. There is also anerror generator 82. Inputs to the adaptive ANN 80 comprise the extractedcritical signal feature that is output from block 28, the specificengine parameters 30, and an error signal from the error generator 82.

One input to the adaptive ANN 80 comprises the extracted critical signalfeature. The extracted critical signal feature is output from block 28and comprises the data sample that has passed through the decompositionportion 34 of the wavelet filter and been compressed and stored asdescribed previously, and shown in block 26 (FIG. 2). The compresseddata sample is reconstructed, as shown in block 36, and the features areextracted, as shown in block 28. The critical signal feature is input tothe adaptive ANN 80. The input also includes corresponding specificengine parameters 30, comprising engine speed, engine load, sparkadvance and EGR fraction, as described previously. The output of theadaptive ANN 80 is described as a supervised output.

The inputs to the error generator 82 comprise the supervised output ofthe adaptive ANN 80 and a desired output 84, which is an externalmeasure of the desired combustion characteristic. The error generator 82calculates a difference between the supervised output and the desiredoutput 84. The output of the error generator 82 becomes an input to theadaptive ANN 80.

Analog training data, comprised of the ionization signal, specificengine parameters, and a signal representing the desired output 84, iscollected using engines and vehicles operating under specific testconditions that preferably have been created based upon a designedexperiment. The signal representing the desired output 84 is comprisedof an output of a wide range air/fuel ratio sensor (not shown) when thecombustion characteristic for control is air/fuel ratio. The signalrepresenting the desired output 84 is comprised of a measure of cylinderpressure when the combustion characteristic for control is the locationof peak pressure.

The analog training data is processed through the analog data sampler20. The ionization data sample is passed through the decompositionsegment 34 of the wavelet filter 24 and compressed and stored for ANNtraining, as shown in block 26. A complete data set includes an outputof the decomposition element 34 of the wavelet filter 24, the desiredoutput 84, and associated engine operating parameters 30 that arecaptured concurrently using the sampling and processing scheduler 22. Afile containing the ionization signal and specific engine parameters andthe specific desired output 84 is compressed and stored digitally. Thecollected data is input to the adaptive ANN 80, which in turn generatesa supervised output 86. The desired output 84 and the supervised output86 represent the specific combustion characteristic that the ANN isbeing trained to identify and quantify. The error generator compares thesupervised output 86 to the desired output 84 and an error signal isgenerated which is input to the adaptive ANN 80. The adaptive ANN 80changes the specific coefficients and biases of the internal equations,until the desired output 84 matches the supervised output for thespecific set of operating conditions. This process is repeated using allthe data collected to train the adaptive ANN 80. The coefficients andbiases created using the adaptive ANN 80 are captured and stored for useby the ANN 32 in a production implementation of the ANN 32. The trainingand use of an ANN is well known to one skilled in the art.

The controller 10 uses the ANN 60 to predict an output based uponreal-time data inputs that are gathered during each engine combustionevent. An output of the artificial neural net 32 is a combustioncharacteristic of the engine, which is used by the controller 10 tocontrol the engine 5. It can be an air/fuel ratio, or a location of peakpressure, or other combustion characteristic that is correlated toengine performance. The neural network in this embodiment wasimplemented using Simulink, DSP Blockset, and Neural Network toolboxesfrom Mathworks, Inc. ANNs are generally known to one skilled in the art.

To extract air/fuel ratio information, the sampling and processingscheduler 22 causes the analog data sampler 20 to sample the ionizationsignal during the initial, flame-phase of the combustion process, whichoccurs 10-30 degrees after start of spark event. This is shown in FIG. 4as item 120. The sampled ionization signal becomes an input into thewavelet filter for extraction of the critical signal feature, which isan integral of the flame phase and peak magnitude of the ionizationsignal. The extracted critical signal feature from the feature extractor28 is then combined with the specific engine operating parameters 30 forinput to the ANN 32. The output of the ANN is a real-time measure ofair/fuel ratio that is input to the controller 10 and able to be used byother engine management algorithms.

To extract information regarding a location of peak pressure, thesampling and processing scheduler 22 causes the analog data sampler 20to sample the ionization signal during the post-phase of the combustionprocess, which typically occurs 20-50 degrees after start of sparkevent. This is shown in FIG. 4 as item 140. The sampled ionizationsignal becomes an input into the wavelet filter for extraction of thecritical signal feature, which is the relative time of thepost-combustion ion peak of the ionization signal. The extractedcritical signal feature from the feature extractor 28 is then combinedwith the specific engine operating parameters 30 for input to the ANN32. The output of the ANN is a real-time measure of a location of peakpressure that is input to the controller 10 for control of the engine 5,and may also be used by other engine management control algorithms.

Although this is described as a method and system for controlling aninternal combustion engine by determining a combustion characteristicfrom an ionization signal using a wavelet filter and an ANN, it isunderstood that an alternate embodiment of this invention includes theuse of the combustion characteristic to detect engine and componentmalfunctions and diagnose failure modes of the engine or components.

It is also understood that the invention applies to the detection ofother critical signal features that can be extracted from the ionizationdata sample, using other wavelet transforms.

It is also understood that the determined combustion characteristic caninclude other combustion characteristics, for example a percentage ofmass fraction burned, NOx content, and EGR mass-fraction dilution.

Although the preferred embodiment describes as a method and system forcontrolling an conventional control system for an internal combustionengine, it is understood that the invention can be implemented inconjunction with other engine control systems such as lean-burn systems,direct-injection fuel systems, cylinder deactivation systems, amongothers.

Although described as a system for use in control of a spark-ignitioninternal combustion engine, it is understood that this invention appliesequally to other forms of internal combustion engines wherein anionization signal may be detected in the combustion chamber. Alternativeengines that are included in this invention include, for example,compression ignition engines and homogeneous charge combustion ignitionengines. When the invention is applied to a compression-ignition engine,the ionization sensor can comprise a glow plug that is operablyconnected to an ion-sense ignition module 12 that is integral to thecontroller.

It is also understood that the invention encompasses an internalcombustion engine, regardless of the specific application. Some typicalapplications can include, for example, automobiles, trucks, boats,ships, agricultural tractors, construction equipment, stationaryengines, motorcycles, racing vehicles, and others.

It is also understood that although the data analysis is described usingalgorithms executed in an electronic controller, the invention alsocovers data analysis that is accomplished using other means, forexample, electronic digital signal processing (DSP) chips. It alsoencompasses data analysis that is accomplished using a combination ofalgorithms and electronic chips and circuits.

The invention has been described with specific reference to thepreferred embodiments and modifications thereto. Further modificationsand alterations may occur to others upon reading and understanding thespecification. It is intended to include all such modifications andalterations insofar as they come within the scope of the invention.

1. A method for controlling an internal combustion engine, comprisingdetermining a combustion characteristic of an individual combustionevent of the engine by: concurrently sampling an ionization signal anddetermining at least one engine operating parameter during a combustionevent of the internal combustion engine, the at least one engineoperating parameter including, but not limited to, an engine load, aspark advance, an amount of delivered fuel and a mass fraction of EGRdelivered, transforming the ionization signal with a wavelet filter,extracting at least one critical signal feature from the ionizationsignal that has been transformed by the wavelet filter, tuning thewavelet filter to the at least one critical signal feature, decomposingthe ionization signal using the tuned wavelet filter, reconstructing thedecomposed ionization signal, processing the at least one criticalsignal feature reconstructed ionization signal and the at least oneengine operating parameter through an artificial neural network; andcontrolling the internal combustion engine based upon the combustioncharacteristic.
 2. The method of claim 1, wherein determining acombustion characteristic of an individual combustion event of theengine occurs each combustion event.
 3. (Cancelled)
 4. The method ofclaim 1, wherein concurrently sampling an ionization signal and at leastone engine parameter during a combustion event of the internalcombustion engine comprises sampling during a critical period of timeafter an ignition event.
 5. The method of claim 1, wherein concurrentlysampling an ionization signal and at least one engine parameter during acombustion event comprises using a sampling and processing scheduler toactivate an analog data sampler during a time when the critical signalfeature is present in the ionization signal.
 6. (Cancelled)
 7. A systemto control an internal combustion engine, including at least one of acompression-ignition engine a spark-ignition engine, and a homogenouscharge compression-ignition engine, the system comprising a controlleroperably connected to: at least one output device of the internalcombustion engine, including engine torque, at least one ion sensoroperable to measure an ionization signal from a combustion event of theinternal combustion engine, and at least one engine sensor operable tomeasure at least one engine operating parameter; wherein said controlleris operable to: concurrently sample an ionization signal using the atleast one ion sensor and determine at least one engine operatingparameter using the at least one engine sensor during a combustionevent, transform the ionization signal with a wavelet filter, extract atleast one critical signal feature from the ionization signal that hasbeen transformed by the wavelet filter, and process the at least onecritical signal feature and the at least one engine operating parameterthrough an artificial neural network to determine at least onecombustion characteristic of the engine; and wherein the controllercontrols the engine torque based upon the at least one combustioncharacteristic of the engine.
 8. (Cancelled)
 9. (Cancelled) 10.(Cancelled)
 11. (Cancelled)